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import streamlit as st
import numpy as np
import pickle
import streamlit.components.v1 as components
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
# Load the pickled model
def load_model():
return pickle.load(open('Credit_Card_Classification_LogisticRegression.pkl','rb'))
# Function for model prediction
def model_prediction(model, features):
predicted = str(model.predict(features)[0])
return predicted
def transform(text):
text = le.fit_transform(text)
return text[0]
def app_design():
# Add input fields for High, Open, and Low values
image = 'credit.png'
st.image(image, use_column_width=True)
st.subheader("Enter the following values:")
Gender = st.selectbox("Gender",('Male','Female'))
if Gender == 'Male':
Gender = 1
else:
Gender = 0
Age= st.number_input("Age")
Debt= st.number_input("Debt")
Married= st.selectbox("Married",('Yes','No'))
if Married == 'Yes':
Married = 1
else:
Married = 0
BankCustomer= st.number_input("Bank Customer")
Industry= st.text_input("Industry")
Industry = transform([Industry])
Ethnicity= st.text_input("Ethnicity")
Ethnicity = transform([Ethnicity])
YearsEmployed = st.number_input("Years Employed")
PriorDefault= st.selectbox("Prior Default",('Yes','No'))
if PriorDefault == 'Yes':
PriorDefault = 1
else:
PriorDefault = 0
Employed= st.selectbox("Employed",('Yes','No'))
if Employed == 'Yes':
Employed = 1
else:
Employed = 0
CreditScore = st.number_input("Credit Score")
DriversLicense= st.selectbox("Drivers License",('Yes','No'))
if DriversLicense == 'Yes':
DriversLicense = 1
else:
DriversLicense = 0
Citizen= st.selectbox("Citizen",('ByBirth','ByOtherMeans'))
if Citizen == 'ByBirth':
Citizen = 1
else:
Citizen = 0
ZipCode= st.number_input("ZipCode")
Income= st.number_input("Income")
# Create a feature list from the user inputs
features = [[Gender, Age,Debt,Married,BankCustomer,Industry,Ethnicity,YearsEmployed,PriorDefault,Employed,CreditScore,DriversLicense,Citizen,ZipCode,Income]]
# Load the model
model = load_model()
# Make a prediction when the user clicks the "Predict" button
if st.button('Predict Status'):
predicted_value = model_prediction(model, features)
if(predicted_value==1):
st.success(f"The credit card is approved")
else:
st.success(f"The credit card is not approved")
def main():
# Set the app title and add your website name and logo
st.set_page_config(
page_title="Credit Card Classification Model",
page_icon=":chart_with_upwards_trend:",
)
st.title("Welcome to our Credit Card Classification Model!")
app_design()
if __name__ == '__main__':
main() |